Enhancing PEM fuel cell efficiency through bio-inspired MPPT under variable operating conditions

Abstract

The aim of this paper is to develop and evaluate a nature-inspired metaheuristic strategy for Maximum Power Point Tracking (MPPT) strategy in Proton Exchange Membrane Fuel Cells (PEMFCs), whose efficiency is highly sensitive to dynamic operating conditions such as cell temperature and the partial pressures of hydrogen and oxygen. These fluctuations continually shift the system's Maximum Power Point (MPP), necessitating adaptive control methods to maintain optimal power extraction. This study introduces a novel MPPT technique based on the Horse Herd Optimization Algorithm (HOA), a recent bio-inspired metaheuristic modeled on the social behavior of horse populations. To the best of our knowledge, this work presents the first application of HOA to PEMFC systems. A comprehensive dynamic model is constructed, integrating the electrochemical characteristics of a 50 kW PEMFC stack, a DC-DC boost converter, and an adaptive MPPT controller guided by HOA. The algorithm adjusts the converter's duty cycle by mimicking behavioral mechanisms-such as grazing, hierarchy, sociability, imitation, defense, and roaming-organized across age-based groups to enhance convergence speed and accuracy. The effectiveness of the HOA-based MPPT is benchmarked against the Cuckoo Search Optimization (CSO) method under various conditions, including standard operation, temperature variations (328 K to 348 K), and pressure fluctuations (1.0-2.0 atm). Simulation results using MATLAB/Simulink demonstrate that the HOA algorithm achieves superior performance, with a maximum power point tracking efficiency of 99.7 % compared to 99.64 % for CSO. Additionally, HOA exhibits a significantly faster settling time of 0.0570 s, outperforming CSO's 0.12 s, and maintains comparable rise times (0.0016s) while eliminating voltage and current oscillations. Under varying thermal and pressure conditions, HOA demonstrates exceptional robustness, rapid convergence, and high stability, maintaining optimal power delivery where conventional methods degrade. This work represents the first successful integration of the Horse Herd Optimization Algorithm into MPPT control for PEM fuel cells and demonstrates its superiority over both traditional and intelligent techniques. It offers a highly efficient and adaptive solution, with promising prospects for future scalability and deployment in real-world fuel cell energy management systems.